Intelligent battery sensor for vehicle and method of storing data in sensor

ABSTRACT

Provided is an intelligent battery sensor for a vehicle which detects an overcurrent module generating an abnormal overcurrent within a vehicle. The intelligent battery sensor for a vehicle includes a data packetizing unit configured to extract internal data variables related to detection of an overcurrent module and packetize the extracted internal data variables, a volatile memory configured to temporarily store the packetized internal data variables, a fault and validity diagnosing unit configured to monitor the packetized internal data variables stored in the volatile memory and classify, when a diagnostic trouble code (DTC) related to the abnormal overcurrent is diagnosed, the packetized internal data variables with respect to a diagnosis time of the DTC, and a nonvolatile memory configured to store the classified internal data variables under the control of the fault and validity diagnosing unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 to Korean PatentApplication No. 10-2014-0056454, filed on May 12, 2014, the disclosureof which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present invention relates to a battery sensor for a vehicle and amethod of storing data using the same.

BACKGROUND

FIG. 1 is a view illustrating a related art vehicle battery sensorsystem.

Referring to FIG. 1, the related art vehicle battery sensor system 50includes a vehicle battery 10, an intelligent battery sensor (IBS) 20,an engine controller (or an engine management system (EMS)) 30, and avehicle load 40.

A positive terminal (+) of the vehicle battery 10 is electricallyconnected to the vehicle load 40, and a negative terminal (−) of thevehicle battery 10 is electrically connected to one terminal of a shuntresistor 15.

The IBS 20 is electrically connected to the negative terminal (−) of thevehicle battery 10 through the other terminal of the shunt resistor 15.

The IBS 20 monitors a voltage and a current of the vehicle battery 10using a current flowing in the shunt resistor 15 and a voltagedifference between both ends of the shunt resistor 15. Although notshown, the IBS 20 may monitor an internal temperature, or the like,using an internal temperature sensor.

Monitored information items are transferred to the engine controller 30according to a local interconnect network (LIN) communication 25.

The engine controller 30 checks a status of the vehicle battery 10 usingthe received information and adjusts the vehicle load 40 according to achecking result.

The vehicle load 40 is connected between the positive (+) terminal ofthe vehicle battery 10 and a vehicle chassis serving as a ground GND.The vehicle load 40 includes a generation load G 42 such as a generatoror an alternator, a large current motor driving load M 44 such as amotor-driven power steering (MDPS), an anti-lock breaking system (ABS),or an air suspension (AirSUS), and an electrical load L 46 such as aheadlight lamp, or the like.

The related art vehicle battery sensor system 50, however, does not havea scheme of establishing (or investigating or finding) causes of a faultby itself when the fault occurs.

SUMMARY

Accordingly, the present invention provides an intelligent batterysensor (IBS) for a vehicle capable of establishing a cause of a faultdue to an error of an operation thereof, and a method of storing data inthe sensor.

In one general aspect, an intelligent battery sensor for a vehicle whichdetects an overcurrent module generating an abnormal overcurrent withina vehicle includes: a data packetizing unit configured to extractinternal data variables related to detection of an overcurrent moduleand packetize the extracted internal data variables; a volatile memoryconfigured to temporarily store the packetized internal data variables;a fault and validity diagnosing unit configured to monitor thepacketized internal data variables stored in the volatile memory andclassify, when a diagnostic trouble code (DTC) related to the abnormalovercurrent is diagnosed, the packetized internal data variables withrespect to a diagnosis time of the DTC; and a nonvolatile memoryconfigured to store the classified internal data variables under thecontrol of the fault and validity diagnosing unit.

In another general aspect, a method of storing data in an intelligentbattery sensor for a vehicle which detects an overcurrent modulegenerating an abnormal overcurrent within a vehicle includes: extractinginternal data variables related to detection of the abnormalovercurrent, and packetizing the extracted internal data variables;storing the packetized internal data variables in a volatile memory;monitoring the packetized internal data variables stored in the volatilememory, and diagnosing whether a diagnostic trouble code (DTC) relatedto the abnormal overcurrent has been generated; when the DTC isdiagnosed, classifying the packetized internal data variables withrespect to a diagnosis time of the DTC; and storing the classifiedinternal data variables in a nonvolatile memory.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view illustrating a related art battery sensor system for avehicle.

FIG. 2 is a block diagram illustrating a battery sensor system for avehicle according to an embodiment of the present invention.

FIG. 3 is a detailed block diagram illustrating an internalconfiguration of an intelligent battery sensor (IBS) illustrated in FIG.2.

FIGS. 4 and 5 are views illustrating a circular queue linked listtechnique used in the present invention.

FIG. 6 is a flow chart illustrating a data storage method of analyzing afault factor according to an embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments of the present invention will be described indetail with reference to the accompanying drawings.

The present invention provides a method of establishing a cause of afault due to an overcurrent of a vehicle or an operational error of anintelligent battery sensor (IBS).

Major data of an IBS is periodically monitored. The monitored major dataof the IBS is classified into immediately previous data and immediatelysubsequent data with respect to a fault diagnosis time, temporarilystored in a nonvolatile memory according to a circular queue linkedlist, and subsequently transferred to a nonvolatile member (NVM).

The circular queue linked list applied in the present invention mayeffectively use a memory in an embedded system having a limited memorywith small capacity.

When the method is applied, power is separated and transferred to ananalysis center, as well as the spot in the occurrence of an accident orfailure, a situation before and after a time of a fault may be analyzedby reading stored data, and time required for establishing causes of thefault may be shortened.

Hereinafter, an embodiment of the present invention will be described indetail.

FIG. 2 is a block diagram illustrating a battery sensor system for avehicle according to an embodiment of the present invention.

Referring to FIG. 2, a battery sensor system 100 for a vehicle includesa vehicle battery 110, an intelligent battery sensor (IBS) 120, a smartjunction module (or a smart junction box (SJB)) 130, and vehicle loads152, 154, and 156.

A negative (−) terminal of the vehicle battery 110 is electricallyconnected to the IBS 120, and a positive (+) terminal of the vehiclebattery 110 is connected to switches SW1, SW2, and SW3 designed in frontstages of the vehicle loads 152, 154, and 156 in parallel.

The IBS 120 monitors whether an overall load current C1 of a vehicle isan overcurrent. When an overcurrent is detected, the IBS 120 controls amultiplexer 140 to individually monitor currents C2 of the vehicle loads152, 154, and 156.

When a corresponding vehicle load (hereinafter, referred to as an“overcurrent module”) in which an overcurrent is generated for a periodof time equal to or greater than a preset period of time, the IBS 120transfers information regarding the detected corresponding overcurrentmodule 152, 154, or 156 to the SJB 130 using vehicle networkcommunication 25. Here, the vehicle network communication may becontroller area network (CAN) communication.

In addition to controlling detection of an overcurrent module, when afault is detected, the IBS 120 analyzes a fault factor, and to this end,the IBS 120 includes a fault factor analyzing module 128. The faultfactor analyzing module 128 will be described in detail with referenceto FIG. 3.

The SUB 130 recognizes a corresponding overcurrent module according toovercurrent module information from the IBS 120, and cuts off powerapplied to the recognized overcurrent module. To this end, the SJB 130transfers a switch OFF signal indicating an OFF operation to theswitches SW1, SW2, and SW3 provided between the overcurrent module andthe vehicle battery 110.

FIG. 3 is a detailed block diagram illustrating an internalconfiguration of the IBS illustrated in FIG. 2.

Referring to FIG. 3, the IBS 120 analyzes a fault factor in addition todetection of the overcurrent module.

The fault factor to be analyzed is divided into two types.

One is generation of a diagnostic trouble code 9DTC) according to aninternal data variable of the IBS 120 and the other is generation ofplausibility error of an internal data variable of the BIS.

The IBS 120 determines whether a fault has occurred with respect to thetwo types of fault factors.

When a fault occurs, the IBS 120 stores (backs up), in the nonvolatilememory, IBS internal data variables (i.e., internal data variables ofthe IBS 120) immediately before and immediately after a point in time atwhich a fault is diagnosed with respect to the two types of faultfactors. Here, the IBS internal data variables are defined as variablesdirectly affecting a calculation process for detecting the overcurrentmodules 152, 154, and 156.

Table 1 below shows IBS internal data variables according to anembodiment of the present invention.

TABLE 1 Data size Classification Data name Unit Resolution (byte)validity range Internal data variable of Vbat V 1 mV 2 6~18 V IBS inputfor detection Ibat A 1 Ma 4 −1500~2000   calculation of overcurrent Tic° C. 0.25° C. 2 −40~125 module Internal data variable of Vbat_(—) V 1 mV2 6~18 V IBS used for detection Ibat_(—) A 1 Ma 4 −1500~2000  calculation of overcurrent Tbat ° C. 0.5° C. 2 −40~105 module Soc % 1% 1 0~100 Soh % 1% 1  0~100 MassCurrent_Flag — state 1 0.1

The IBS 120 includes a signal processing module 121 for detecting anovercurreent module, a BTM module 122, a state of charge (SOC) module124, a state of health (SOH) module 126, and an overcurrent moduledetecting unit 129.

The IBS 120 further includes a fault factor analyzing module 128 forstoring (backing up) IBS internal data variables immediately before andimmediately after a fault diagnosis time in a nonvolatile memory.

The signal processing module 121 receives a voltage Vbat and a currentthat from the vehicle battery 110, filters, the received voltage Vbatand the received current that, and outputs the filtered battery voltageVbat_ and the current Ibat_.

The BTM module 122 receives an ASIC temperature Tic and the filteredbattery voltage Vbat_ and current that_ and monitors an internaltemperature of the vehicle battery.

The SOC module 124 receives the filtered battery voltage Vbat_ andcurrent that_ and monitors a charge rate (%) of the vehicle battery.

The SOH module 126 comprehensively determine the filtered batteryvoltage Vbat_ and current Ibat_ and the charge rate (%) of the vehiclebattery monitored by the SOC module 124, to monitor an aging rate (%) ofthe vehicle battery.

The overcurrent module detecting unit 129 detects an overcurrent moduleusing a current (ADC current) calculated on the basis of the monitoringresult values from the BTM module 122, the SOC module 124, and the SOHmodule 126 and a difference in an across voltage of a shunt resistor 22.

The ADC current calculated on the basis of the difference in the acrossvoltage of the shunt resistor 22 is a current obtained by adding thecurrent C1 of the entire load of the vehicle, that is, a current of theG load 152, a current of the M load 154, and a current of the L load 156illustrated in FIG. 2.

When the current C1 of the overall load of the vehicle equal to orgreater than a preset overcurrent threshold value is continuouslydetected (when substantially 200 A continues for more than 10 seconds),the overcurrent module detecting unit 129 outputs a control signal forindividually detecting the load current C2 flowing in each of the loads152, 154, and 156, to the multiplexer 140.

The multiplexer 140 selectively transfers the load current C2 flowing ineach module to the overcurrent module detecting unit 129 according tothe control signal.

The overcurrent module detecting unit 129 analyzes the load current C2of each module, recognizes an overcurrent module in which an overcurrentflows, and transfers a signal indicating the recognized overcurrentmodule to the SBJ 130.

The SJB 130 switches off a switch connecting the correspondingovercurrent module and the vehicle battery 110 according to the signal.

The fault factor analyzing module 128 stores (backs up), in thenonvolatile memory, the IBS internal data variables as illustrated inTable 1 affecting the detection calculation of the overcurrent module.Here, the IBS internal data variables stored (backed up) in thenonvolatile memory 128-7 are classified into data variables immediatelybefore and immediately after each diagnosis time of the two types offault factors described above, and the classified IBS internal datavariables are again classified into two types of fault factors andrespectively stored in a first storage region (DTC storage region) and asecond storage region (validity storage region0 of the nonvolatilememory 128-7.

To this end, the fault factor analyzing module 128 includes a datapacketizing unit 128-1, a volatile memory 128-3, a fault and validitydiagnosing unit 128-5, a nonvolatile memory 128-7, and a processing unit128-9.

The data packetizing unit 128-1 extracts the IBS internal data variablesdirectly affecting the detection calculation until the overcurrentmodule detecting unit 129 detects an overcurrent module, and packetizesthe same.

The packetized IBS internal data variables are sequentially stored in amemory queue of the volatile memory 128-3 according to a circular queuelinked list technique.

The fault and validity diagnosing unit 128-5 monitors (or inspects) thepacketized IBS internal data variables stored in the memory queue of thevolatile memory 128-3 in real time, and diagnoses whether a DTC has beengenerated in the overcurrent module generating an abnormal overcurrent.For example, the fault and validity diagnosing unit 128-5 diagnoseswhether a DTC related to a hardware fault of the multiplexer 140 ordefective I/O control, a fault in a current sensor such as the shuntresistor, or a fault of a CAN communication line has been generated.

When occurrence of a DTC is not diagnosed, the fault and validitydiagnosing unit 128-5 diagnoses a validity error of the IBS internaldata variables. Here, the validity error of the IBS internal datavariables refer to generation of IBS internal data variables not withinthe validity range of Table 1.

After the generation of the DTC of the overcurrent module or thevalidity error (or an error) of the IBS internal data variables arediagnosed, fault and validity diagnosing unit 128-5 classifies apredetermined number of IBS internal data variables, for example, twentypacketized IBS internal data variables immediately before the DTCdiagnosis or immediately before the validity error diagnosis and apredetermined number of packetized IBS internal data variables, forexample, twenty packetized IBS internal data variables, immediatelyafter the DTC generation or immediately after validity error diagnosis,with respect to the diagnosis time of the DTC of the overcurrent moduleand the diagnosis time of the validity error of the IBS internal datavariables.

The packetized IBS internal data variables immediately before andimmediately after the DTC diagnosis or validity error are transferredfrom the memory queue (or memory buffer) the volatile memory 128-5 tothe nonvolatile memory 128-7 and stored therein under the control of thefault and validity diagnosing unit 128-5.

The nonvolatile memory 128-7 is divided into first and second storageregions, and the IBS internal data variables immediately before andimmediately after the DTC diagnosis are stored in the first storageregion, and the IBS internal data variables immediately before andimmediately after the validity error diagnosis are stored in the secondstorage region.

In this manner, the fault factor analyzing module 128 periodicallymonitors the IBS internal data variables immediately before andimmediately after the fault diagnosis stored in the volatile memory128-3, and when a fault occurs in a vehicle, the fault factor analyzingmodule 128 stores the IBS internal data variables immediately before andimmediately after the fault diagnosis in the nonvolatile memory 128-7,thereby allowing fault causes to be precisely analyzed.

Even though the nonvolatile memory 128-7 is physically separated fromthe intelligent battery sensor for a vehicle, the data stored in thenonvolatile memory 128-7 does not become extinct, and thus, the faultfactors may be precisely analyzed any time and anywhere.

The volatile memory 128-3 such as a RAM is limited in storage capacitydue to a purpose thereof, and thus, a scheme of effectively usingstorage capacity of the volatile memory 128-3 is required.

Thus, in this embodiment, a method of sequentially storing the IBSinternal data variables in the volatile memory using the circular queuelinked list as described above may be used.

In the method of sequentially storing data in a computing system, amemory queue using an array list may be utilized. The memory queue usingan array list is a scheme of sequentially storing data in apredetermined storage region of the volatile memory 128-3.

As illustrated in FIG. 4, the array list technique allows for easilyreferring to data and fast access in managing memory. However, since amemory size should be determined at an early stage, memory loss(overflow) is made, and when data is inserted or deleted, the overalldata should be moved.

In particular, utilization of a dynamic allocation scheme available forallocation and release in terms of management of limited storagecapacity allows for more effective management. A linked list is astructure complementing the shortcomings of the array scheme.

The structure of the linked list is illustrated in FIG. 5.

As illustrated in FIG. 5, the linked list includes a plurality of nodes(Node 1, Node 2, . . . , Node N), and each node includes data (or databundle) and a link address of other data. A structure of connecting thenodes (Node 1, Node 2, . . . , Node N) is the linked list.

The linked list has a slightly complicated structure, compared with theforegoing array list, but it is free to allocate the number of nodes(Node 1, Node 2, . . . , Node N), namely, memory, and if not necessary,the linked list may be immediately released, reducing memoryconsumption.

Also, in inserting data, addition/deletion may be performed only with alink address, rather than moving the entirety, allowing for fastprocessing in terms of controlling.

In an embodiment of the present invention, data may be stored in every10 ms. Thus, in order to store about 20-byte data node in the volatilememory 128-3 at every 10 ms, theoretically, a memory having infinitestorage capacity is required. However, in the data storage schemeaccording to an embodiment of the present invention, since the IBSinternal data variables immediately before and immediately after a faultis generated, only a memory of about 40 nodes, namely, 20 bytes×40nodes=800 bytes, may be utilized for required data.

The structure of the linked list is designed to promote efficacy ofmemory management, and thus, the data storage scheme according to anembodiment of the present invention may be simply realized. For example,it may be realized by allowing a link address of the final node (e.g.,address 2xx) to indicate the first node address in FIG. 5.

FIG. 6 is a flow chart illustrating a data storage method of analyzing afault factor according to an embodiment of the present invention.

Referring to FIG. 6, first, in step S410, an input signal is processed.Here, the input signal is data directly affecting detection calculationof an overcurrent module among the BTM module, the SOC module, and theSOH module.

Next, in step S412, IBS internal data variables used for detecting theovercurrent module in processing the input signal are extracted.

Subsequently, in step S414, the extracted IBS internal data variablesare packetized, and in step S416, the packetized IBS internal datavariables are sequentially stored in a memory queue of a volatilememory. Here, in order to store the IBS internal data variables in thememory queue, a circular queue linked list technique may be utilized.

Thereafter, in step S418, the packetized IBS internal data variablesstored in the memory queue are monitored to diagnose whether a DTC hasbeen generated.

In step S420, when a DTC has been generated, a predetermined number of(e.g., twenty) the packetized IBS internal data variables immediatelybefore the DTC diagnosis time and a predetermined number of (e.g.,twenty) the packetized IBS internal data variables immediately after theDTC diagnosis time are sorted.

In step S422, the sorted packetized IBS internal data variablesimmediately before and immediately after the DTC diagnosis time arestored in a first storage region (DTC storage region0 allocated to anonvolatile memory.

In step S418, when the DTC has not been generated, in step S424, whetherthe IBS internal data variables have a validity error is diagnosed.

In step S426, when a validity error of the IBS internal data variablesis diagnosed, a predetermined number of the IBS internal data variablesimmediately before the diagnosis time of the validity error of the IBSinternal data variables and a predetermined number of the IBS internaldata variables immediately after the diagnosis time of the validityerror of the IBS internal data variables are sorted.

In step S428, the sorted IBS internal data variables are stored in asecond storage region (validity storage region) of the nonvolatilememory.

So far the configuration of the present invention has been described indetail with reference to embodiments the accompanying drawings, but thisis merely illustrative and various modifications may be made withoutdeparting from the scope of the present invention. For example, in theembodiment of FIG. 3, the structure in which the volatile memory 128-3and the validity diagnosing unit 128-5 are separated is illustrated, butthe volatile memory 128-3 may be provided within the fault and validitydiagnosing unit 128-5. In this case, IBS internal data variablespacketized by the data packet unit 128-1 may directly be transferred tothe fault and validity diagnosing unit 128-5. Thus, the scope of thepresent invention should not be determined to be limited to thedescribed embodiments of the present disclosure but be determined byclaims and equivalents thereof, as well as claims.

According to the present invention, fault causes due to an IBS operationerror may be established by analyzing data stored in a nonvolatilememory within the IBS. Thus, a period of time for establishing faultcauses can be shortened. Also, since the limited volatile memory iseffectively utilized, the present invention may also be variouslyapplied to establish fault causes of other ECU.

A number of exemplary embodiments have been described above.Nevertheless, it will be understood that various modifications may bemade. For example, suitable results may be achieved if the describedtechniques are performed in a different order and/or if components in adescribed system, architecture, device, or circuit are combined in adifferent manner and/or replaced or supplemented by other components ortheir equivalents. Accordingly, other implementations are within thescope of the following claims.

What is claimed is:
 1. An intelligent battery sensor for a vehicle whichdetects an overcurrent module generating an abnormal overcurrent withinthe vehicle, the intelligent battery sensor comprising: a datapacketizing unit configured to extract internal data variables relatedto detection of an overcurrent module, and packetize the extractedinternal data variables; a volatile memory configured to temporarilystore the packetized internal data variables; a fault and validitydiagnosing unit configured to monitor the packetized internal datavariables stored in the volatile memory, and classify a first number ofthe packetized internal data variables immediately before a diagnosis ofa diagnostic trouble code (DTC) and a second number of the packetizedinternal data variables immediately after the diagnosis of the DTC, inresponse to the diagnosis of the DTC, wherein the DTC is related to theabnormal overcurrent; and a nonvolatile memory configured to store theclassified first and second numbers of the internal data variables undercontrol of the fault and validity diagnosing unit.
 2. The intelligentbattery sensor of claim 1, wherein the fault and validity diagnosingunit is further configured to diagnose a validity error for determiningwhether the internal data variables are outside of a validity range, andto classify the packetized internal data variables with respect to adiagnosis time of the validity error.
 3. The intelligent battery sensorof claim 2, wherein the fault and validity diagnosing unit is furtherconfigured to classify a third number of the packetized internal datavariables immediately before the diagnosis of the validity error and afourth number of the packetized internal data variables immediatelyafter the diagnosis of the validity error, with respect to the diagnosistime of the validity error.
 4. The intelligent battery sensor of claim3, wherein the nonvolatile memory is further configured to store theclassified third and fourth numbers of the internal data variables undercontrol of the fault and validity diagnosing unit.
 5. The intelligentbattery sensor of claim 3, wherein the nonvolatile memory comprises afirst storage region configured to store the classified first and secondnumbers of the internal data variables, and a second storage regionconfigured to store the classified third and fourth numbers of theinternal data variables.
 6. The intelligent battery sensor of claim 1,wherein the packetized internal data variables are temporarily stored inthe volatile memory according to a circular queue linked list technique.7. A method of storing data in an intelligent battery sensor for avehicle which detects an overcurrent module generating an abnormalovercurrent within the vehicle, the method comprising: extractinginternal data variables related to detection of the abnormalovercurrent, and packetizing the extracted internal data variables;storing the packetized internal data variables in a volatile memory;monitoring the stored packetized internal data variables, and diagnosingwhether a diagnostic trouble code (DTC) related to the abnormalovercurrent is generated; classifying a first number of the packetizedinternal data variables immediately before the diagnosis of the DTC anda second number of the packetized internal data variables immediatelyafter the diagnosis of the DTC, in response to the DTC being diagnosed;and storing the classified first and second numbers of the internal datavariables in a nonvolatile memory.
 8. The method of claim 7, wherein thediagnosing of whether the DTC is generated comprises diagnosing avalidity error for determining whether the internal data variables areoutside of a validity range, and the classifying comprises classifyingthe packetized internal data variables with respect to a diagnosis timeof the validity error.
 9. The method of claim 8, wherein the classifyingcomprises classifying a third number of the packetized internal datavariables immediately before the diagnosis of the validity error and afourth number of the packetized internal data variables immediatelyafter the diagnosis of the validity error, with respect to the diagnosistime of the validity error.
 10. The method of claim 9, wherein thestoring comprises storing the classified third and fourth numbers of theinternal data variables.
 11. The method of claim 9, wherein the storingcomprises storing the classified first and second numbers of theinternal data variables in a first storage region of the nonvolatilememory, and storing the classified third and fourth numbers of theinternal data variables in a second storage region of the nonvolatilememory.
 12. The method of claim 7, wherein the storing of the packetizedinternal data variables in the volatile memory comprises storing thepacketized internal data variables in the volatile memory according to acircular queue linked list technique.
 13. An intelligent battery sensorfor a vehicle which detects an overcurrent module generating an abnormalovercurrent within the vehicle, the intelligent battery sensorcomprising: a data packetizing unit configured to extract internal datavariables related to detection of an overcurrent module and packetizethe extracted internal data variables; a volatile memory configured totemporarily store the packetized internal data variables; a fault andvalidity diagnosing unit configured to monitor the packetized internaldata variables stored in the volatile memory and classify, in responseto a diagnostic trouble code (DTC) related to the abnormal overcurrentbeing diagnosed, the packetized internal data variables with respect toa diagnosis time of the DTC, diagnose a validity error for determiningwhether the internal data variables are outside of a validity range,classify the packetized internal data variables with respect to adiagnosis time of the validity error, and classify a predeterminednumber of packetized internal data variables immediately before thediagnosis of the validity error and a predetermined number of packetizedinternal data variables immediately after the diagnosis of the validityerror, with respect to the diagnosis time of the validity error; and anonvolatile memory configured to store the classified internal datavariables under control of the fault and validity diagnosing unit.